TY - CONF AB - Aufgrund der Fortschritte der Digitalisierung finden Systeme zur Zustandsüberwachung vermehrt Einsatz in der Industrie, um durch eine zustandsbasierte oder eine prädiktive Instandhaltung Vorteile, wie eine verbesserte Zuverlässigkeit und geringere Kosten zu erzielen. Dabei beruhen Zustandsüberwachungssysteme auf den folgenden Bausteinen: Sensorik, Datenvorverarbeitung, Merkmalsextraktion und -auswahl, Diagnose bzw. Prognose sowie einer Entscheidungsfindung basierend auf den Ergebnissen. Jeder dieser Bausteine erfordert individuelle Einstellungen, um ein geeignetes Zustandsüberwachungssystem für die jeweilige Anwendung zu entwickeln. Eine offene Fragestellung im Bereich der Zustandsüberwachung ergibt sich aufgrund der Unsicherheit der Zukunft, die sich in den zukünftigen Betriebs- und Umgebungsbedingungen zeigt. Diese Unsicherheit gilt es in allen Bausteinen zu berücksichtigen. Dieser Beitrag konzentriert sich auf den Baustein Merkmalsextraktion und -selektion, mit dem Ziel anhand geeigneter Merkmale eine Prognose der nutzbaren Restlebensdauer mit hoher Genauigkeit realisieren zu können. Daher werden geeignete Merkmale aus dem Zeitbereich und daraus abgeleitete Zustandsindikatoren für die Restlebensdauerprognose von technischen Systemen vorgestellt. Dabei sind Zustandsindikatoren Kenngrößen zur Beobachtung des Zustands der kritischen Systemkomponenten. Anhand dreier Anwendungsbeispiele wird ihre Eignung evaluiert. Dabei werden Daten aus Lebensdauerversuchen unter instationären Betriebs- und Umgebungsbedingungen ausgewertet. Die auftretenden Unsicherheiten der Zukunft werden somit berücksichtigt. Die Beispielsysteme beruhen auf Gummi-Metall-Elementen und Wälzlagern. Aus den generierten Ergebnissen lässt sich schließen, dass die Zustandsindikatoren aus der betrachteten Zeitreihen-Toolbox auch unter unbekannten Betriebs- und Umgebungsbedingungen robust sind. AU - Aimiyekagbon, Osarenren Kennedy AU - Bender, Amelie AU - Sextro, Walter ID - 27652 KW - run-to-failure KW - rubber-metal element KW - bearing prognostics KW - non-stationary operating conditions KW - varying operating conditions KW - feature extraction KW - feature selection SN - 0083-5560 T2 - VDI-Berichte 2391 TI - Extraktion und Selektion geeigneter Merkmale für die Restlebensdauerprognose von technischen Systemen trotz aleatorischen Unsicherheiten ER - TY - CONF AB - Several methods, including order analysis, wavelet analysis and empirical mode decomposition have been proposed and successfully employed for the health state estimation of technical systems operating under varying conditions. However, where information such as the speed of rotating machinery, component specifications or other domain-specific information is unavailable, such methods are often infeasible. Thus, this paper investigates the application of classical time-domain features, features from the medical field and novel features from the highly comparative time-series analysis (HCTSA) package, for the health state estimation of rotating machinery operating under varying conditions. Furthermore, several feature selection methods are investigated to identify features as viable health indicators for the diagnostics and prognostics of technical systems. As a case study, the presented methods are evaluated on real-world and experimentally acquired vibration data of bearings operating under varying speed. The results show that the selected features can successfully be employed as health indicators for technical systems operating under varying conditions. AU - Aimiyekagbon, Osarenren Kennedy AU - Bender, Amelie AU - Sextro, Walter ID - 22507 KW - Wind turbine diagnostics KW - bearing diagnostics KW - non-stationary operating conditions KW - varying operating conditions KW - feature extraction KW - feature selection KW - fault detection KW - failure detection T2 - Proceedings of the Seventeenth International Conference on Condition Monitoring and Asset Management (CM 2021) TI - On the applicability of time series features as health indicators for technical systems operating under varying conditions ER - TY - CONF AB - The continuous refinement of sensor technologies enables the manufacturing industry to capture increasing amounts of data during the production process. As processes take time to complete, sensors register large amounts of time-series-like data for each product. In order to make this data usable, a feature extraction is mandatory. In this work, we discuss and evaluate different network architectures, input pre-processing and cost functions regarding, among other aspects, their suitability for time series of different lengths. AU - Thiel, Christian AU - Steidl, Carolin AU - Henning, Bernd ID - 15488 KW - Dynamic Time Warping KW - Feature Extraction KW - Masking KW - Neural Networks SN - 978-3-9819376-0-2 T2 - 20. GMA/ITG-Fachtagung. Sensoren und Messsysteme 2019 TI - P2.9 Comparison of deep feature extraction techniques for varying-length time series from an industrial piercing press ER - TY - CONF AU - Boschmann, Alexander AU - Agne, Andreas AU - Witschen, Linus Matthias AU - Thombansen, Georg AU - Kraus, Florian AU - Platzner, Marco ID - 15873 KW - Electromyography KW - Feature extraction KW - Delays KW - Hardware Pattern recognition KW - Prosthetics KW - High definition video SN - 9781467394062 T2 - 2015 International Conference on ReConFigurable Computing and FPGAs (ReConFig) TI - FPGA-based acceleration of high density myoelectric signal processing ER - TY - CONF AB - With the paradigm shift towards prognostic and health management (PHM) of machinery, there is need for reliable PHM methodologies with narrow error bounds to allow maintenance engineers take decisive maintenance actions based on the prognostic results. Prognostics is mainly concerned with the estimation of the remaining useful life (RUL) or time to failure (TTF). The accuracy of PHM methods is usually a function of the features extracted from the raw data obtained from sensors. In cases where the extracted features do not display clear degradation trends, for instance highly loaded bearings, the accuracy of the state of the art PHM methods is significantly affected. The data which lacks clear degradation trend is referred to as non-trending data. This study presents a method for extracting degradation trends from non-trending condition monitoring data for RUL estimation. The raw signals are first filtered using a discrete wavelet transform (DWT) denoising filter to remove noise from the acquired signals. Time domain, frequency domain and time-frequency domain features are then extracted from the filtered signals. An autoregressive model is then applied to the extracted features to identify the degradation trends. Features representing the maximum health information are then selected based on a performance evaluation criteria using extreme learning machine (ELM) algorithm. The selected features can then be used as inputs in a prognostic algorithm. The feasibility of the method is demonstrated using experimental bearing vibration data. The performance of the method is evaluated on the accuracy of RUL estimation and the results show that the method can be used to accurately estimate RUL with a maximum error of 10\%. AU - Kimotho, James Kuria AU - Sextro, Walter ID - 9880 KW - autoregressive model ELM feature extraction feature selection non-trending Remaining useful Life T2 - Proceedings of the Second European Conference of the Prognostics and Health Management Society 2014 TI - An approach for feature extraction and selection from non-trending data for machinery prognosis VL - 5 ER -